In a conventional system, an initial plan for a team of unmanned autonomous vehicles (UAVs) may be generated at the beginning of a mission as a single long chain of steps. Each of the steps may be a primitive item performed without additional calculation. When changes in an environment occur, the conventional system may require a change to some of the steps in the initial plan. The system would then re-determine the entire plan from that point on. The Replanning may take a fairly long period of time.
In a time critical environment, it may be crucial that replanning occur quickly (i.e., before catastrophic situations occur, etc.). Frequent, time-consuming replanning thus bog the conventional system down, leaving critical decisions to already overloaded human commanders. By combining a centrally controlled, deliberative model and a swarm model, timing constraints may be relaxed and flexibility of the system increased.
Another conventional planning system may direct a number of homogeneous vehicles to execute a mission plan. The complexity of the mission plan required is greatly increased when vehicles are non-homogeneous (i.e., different capacities for perception, situational awareness, analysis and decision making, as well as different communication methods, etc.).
These conventional systems rely heavily on humans to prepare mission plans and monitor execution with only limited use of planning aids. Conventional planning aids attempt automated planning by utilizing traditional models such as batch processes, sense and act procedures, etc. However, these planning aids require relatively long advance preparation time, based either on static or predicted feedback. Also, these conventional aids provide only limited ability to process complex, large dimension problems and to quickly refine or replan based on unfolding dynamic events that typically are the norm, rather than the exception, for most environments, especially urban environments.